Enable the XCS to Dynamically Learn Multiple Problems
Yao‐Ming Wu, Liangyu Chen, Po-Ming Lee, Tzu‐Chien Hsiao · 2015
The field of presentation of Extended Classifier System(XCS) has undergone many fluctuations and shifts over the years to adapt different domain problems. With the increasing usage of application of artificial intelligence requirements for more complexity presentations of XCS have become more critical. And learning multiple problems is a subject that was needed but few one studied. To dynamically learn multiple problems during a single learning process, we applied a novel representation of classifier conditions of the XCS, named Sensory Tag (ST) to achieve this goal. The XCS with the ST as the input representation is called XCSSTC. The experiments of the proposed method were conducted for the single step problems. The potential of XCSSTC's use in future application of artificial intelligence clearly needs further exploration.